基于小波包最优基分解的脑电图脑机接口特征提取
Feature extraction for EEG-based brain-computer interfaces by wavelet packet best basis decomposition.
作者信息
Yang Bang-hua, Yan Guo-zheng, Yan Rong-guo, Wu Ting
机构信息
School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China.
出版信息
J Neural Eng. 2006 Dec;3(4):251-6. doi: 10.1088/1741-2560/3/4/001. Epub 2006 Sep 5.
A method based on wavelet packet best basis decomposition (WPBBD) is investigated for the purpose of extracting features of electroencephalogram signals produced during motor imagery tasks in brain-computer interfaces. The method includes the following three steps. (1) Original signals are decomposed by wavelet packet transform (WPT) and a wavelet packet library can be formed. (2) The best basis for classification is selected from the library. (3) Subband energies included in the best basis are used as effective features. Three different motor imagery tasks are discriminated using the features. The WPBBD produces a 70.3% classification accuracy, which is 4.2% higher than that of the existing wavelet packet method.
为了提取脑机接口中运动想象任务期间产生的脑电图信号的特征，研究了一种基于小波包最优基分解（WPBBD）的方法。该方法包括以下三个步骤。（1）通过小波包变换（WPT）对原始信号进行分解，形成小波包库。（2）从库中选择用于分类的最优基。（3）将最优基中包含的子带能量用作有效特征。使用这些特征对三种不同的运动想象任务进行区分。WPBBD产生了70.3%的分类准确率，比现有的小波包方法高4.2%。
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